Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations18221
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory104.0 B

Variable types

Categorical2
Numeric11

Alerts

10 km Czas is highly overall correlated with 10 km Tempo and 8 other fieldsHigh correlation
10 km Tempo is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
15 km Czas is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
15 km Tempo is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
20 km Czas is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
20 km Tempo is highly overall correlated with 10 km Czas and 9 other fieldsHigh correlation
5 km Czas is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
5 km Tempo is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
Czas is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
Kategoria wiekowa is highly overall correlated with PłećHigh correlation
Płeć is highly overall correlated with Kategoria wiekowaHigh correlation
Tempo is highly overall correlated with 10 km Czas and 8 other fieldsHigh correlation
Tempo Stabilność is highly overall correlated with 20 km TempoHigh correlation

Reproduction

Analysis started2025-11-09 20:36:11.931630
Analysis finished2025-11-09 20:36:48.778954
Duration36.85 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Płeć
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.5 KiB
M
12959 
K
5262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18221
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M12959
71.1%
K5262
28.9%

Length

2025-11-09T21:36:48.970125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-09T21:36:49.385206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
m12959
71.1%
k5262
28.9%

Most occurring characters

ValueCountFrequency (%)
M12959
71.1%
K5262
28.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter18221
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M12959
71.1%
K5262
28.9%

Most occurring scripts

ValueCountFrequency (%)
Latin18221
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M12959
71.1%
K5262
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII18221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M12959
71.1%
K5262
28.9%

Kategoria wiekowa
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size142.5 KiB
M40
4366 
M30
4185 
M20
2368 
K30
1863 
K40
1799 
Other values (8)
3640 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54663
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM30
2nd rowM20
3rd rowM40
4th rowM20
5th rowM30

Common Values

ValueCountFrequency (%)
M404366
24.0%
M304185
23.0%
M202368
13.0%
K301863
10.2%
K401799
9.9%
M501456
 
8.0%
K201101
 
6.0%
M60511
 
2.8%
K50417
 
2.3%
K6075
 
0.4%
Other values (3)80
 
0.4%

Length

2025-11-09T21:36:49.543742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m404366
24.0%
m304185
23.0%
m202368
13.0%
k301863
10.2%
k401799
9.9%
m501456
 
8.0%
k201101
 
6.0%
m60511
 
2.8%
k50417
 
2.3%
k6075
 
0.4%
Other values (3)80
 
0.4%

Most occurring characters

ValueCountFrequency (%)
018221
33.3%
M12960
23.7%
46165
 
11.3%
36048
 
11.1%
K5261
 
9.6%
23469
 
6.3%
51873
 
3.4%
6586
 
1.1%
778
 
0.1%
82
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36442
66.7%
Uppercase Letter18221
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018221
50.0%
46165
 
16.9%
36048
 
16.6%
23469
 
9.5%
51873
 
5.1%
6586
 
1.6%
778
 
0.2%
82
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M12960
71.1%
K5261
28.9%

Most occurring scripts

ValueCountFrequency (%)
Common36442
66.7%
Latin18221
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
018221
50.0%
46165
 
16.9%
36048
 
16.6%
23469
 
9.5%
51873
 
5.1%
6586
 
1.6%
778
 
0.2%
82
 
< 0.1%
Latin
ValueCountFrequency (%)
M12960
71.1%
K5261
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII54663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018221
33.3%
M12960
23.7%
46165
 
11.3%
36048
 
11.1%
K5261
 
9.6%
23469
 
6.3%
51873
 
3.4%
6586
 
1.1%
778
 
0.1%
82
 
< 0.1%

5 km Czas
Real number (ℝ)

High correlation 

Distinct1256
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1668.6652
Minimum0
Maximum3825
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size142.5 KiB
2025-11-09T21:36:49.792572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1283
Q11503
median1659
Q31829
95-th percentile2073
Maximum3825
Range3825
Interquartile range (IQR)326

Descriptive statistics

Standard deviation238.12104
Coefficient of variation (CV)0.14270151
Kurtosis0.53082143
Mean1668.6652
Median Absolute Deviation (MAD)163
Skewness0.18863224
Sum30404748
Variance56701.628
MonotonicityNot monotonic
2025-11-09T21:36:50.142496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165972
 
0.4%
156453
 
0.3%
164448
 
0.3%
168245
 
0.2%
152945
 
0.2%
164043
 
0.2%
159543
 
0.2%
159643
 
0.2%
160942
 
0.2%
163742
 
0.2%
Other values (1246)17745
97.4%
ValueCountFrequency (%)
01
< 0.1%
9071
< 0.1%
9231
< 0.1%
9371
< 0.1%
9471
< 0.1%
9691
< 0.1%
9711
< 0.1%
9721
< 0.1%
9761
< 0.1%
9901
< 0.1%
ValueCountFrequency (%)
38251
< 0.1%
34671
< 0.1%
31801
< 0.1%
31521
< 0.1%
25271
< 0.1%
25261
< 0.1%
24721
< 0.1%
24581
< 0.1%
24321
< 0.1%
24231
< 0.1%

5 km Tempo
Real number (ℝ)

High correlation 

Distinct1256
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5622172
Minimum0
Maximum12.75
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size142.5 KiB
2025-11-09T21:36:50.441571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2766667
Q15.01
median5.53
Q36.0966667
95-th percentile6.91
Maximum12.75
Range12.75
Interquartile range (IQR)1.0866667

Descriptive statistics

Standard deviation0.79373679
Coefficient of variation (CV)0.14270151
Kurtosis0.53082143
Mean5.5622172
Median Absolute Deviation (MAD)0.54333333
Skewness0.18863224
Sum101349.16
Variance0.63001809
MonotonicityNot monotonic
2025-11-09T21:36:50.741250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5372
 
0.4%
5.21333333353
 
0.3%
5.4848
 
0.3%
5.60666666745
 
0.2%
5.09666666745
 
0.2%
5.46666666743
 
0.2%
5.31666666743
 
0.2%
5.3243
 
0.2%
5.36333333342
 
0.2%
5.45666666742
 
0.2%
Other values (1246)17745
97.4%
ValueCountFrequency (%)
01
< 0.1%
3.0233333331
< 0.1%
3.0766666671
< 0.1%
3.1233333331
< 0.1%
3.1566666671
< 0.1%
3.231
< 0.1%
3.2366666671
< 0.1%
3.241
< 0.1%
3.2533333331
< 0.1%
3.31
< 0.1%
ValueCountFrequency (%)
12.751
< 0.1%
11.556666671
< 0.1%
10.61
< 0.1%
10.506666671
< 0.1%
8.4233333331
< 0.1%
8.421
< 0.1%
8.241
< 0.1%
8.1933333331
< 0.1%
8.1066666671
< 0.1%
8.0766666671
< 0.1%

10 km Czas
Real number (ℝ)

High correlation 

Distinct2360
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3329.9231
Minimum1853
Maximum5819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.5 KiB
2025-11-09T21:36:51.036560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1853
5-th percentile2549
Q12983
median3306
Q33651
95-th percentile4178
Maximum5819
Range3966
Interquartile range (IQR)668

Descriptive statistics

Standard deviation486.85269
Coefficient of variation (CV)0.14620539
Kurtosis-0.2259953
Mean3329.9231
Median Absolute Deviation (MAD)333
Skewness0.19249351
Sum60674529
Variance237025.54
MonotonicityNot monotonic
2025-11-09T21:36:51.337036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330640
 
0.2%
332829
 
0.2%
331728
 
0.2%
330027
 
0.1%
331026
 
0.1%
321026
 
0.1%
291525
 
0.1%
327125
 
0.1%
308624
 
0.1%
322024
 
0.1%
Other values (2350)17947
98.5%
ValueCountFrequency (%)
18531
 
< 0.1%
18801
 
< 0.1%
18851
 
< 0.1%
19301
 
< 0.1%
19501
 
< 0.1%
19551
 
< 0.1%
19652
< 0.1%
19661
 
< 0.1%
19751
 
< 0.1%
19773
< 0.1%
ValueCountFrequency (%)
58191
< 0.1%
49591
< 0.1%
49441
< 0.1%
49421
< 0.1%
49171
< 0.1%
48511
< 0.1%
48482
< 0.1%
48461
< 0.1%
48141
< 0.1%
48031
< 0.1%

10 km Tempo
Real number (ℝ)

High correlation 

Distinct1368
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5375265
Minimum-0.53666667
Maximum10.406667
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size142.5 KiB
2025-11-09T21:36:51.716664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.53666667
5-th percentile4.2066667
Q14.9333333
median5.4766667
Q36.08
95-th percentile7.0533333
Maximum10.406667
Range10.943333
Interquartile range (IQR)1.1466667

Descriptive statistics

Standard deviation0.8559637
Coefficient of variation (CV)0.1545751
Kurtosis0.13987123
Mean5.5375265
Median Absolute Deviation (MAD)0.57
Skewness0.26731527
Sum100899.27
Variance0.73267386
MonotonicityNot monotonic
2025-11-09T21:36:51.965670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.36666666748
 
0.3%
5.50666666745
 
0.2%
5.48333333345
 
0.2%
5.3643
 
0.2%
5.62666666741
 
0.2%
5.27333333341
 
0.2%
5.42666666740
 
0.2%
5.2840
 
0.2%
5.31666666740
 
0.2%
5.42333333340
 
0.2%
Other values (1358)17798
97.7%
ValueCountFrequency (%)
-0.53666666671
< 0.1%
0.421
< 0.1%
2.2733333331
< 0.1%
2.391
< 0.1%
2.4266666671
< 0.1%
2.6433333331
< 0.1%
2.6566666671
< 0.1%
2.6933333331
< 0.1%
2.7433333331
< 0.1%
2.81
< 0.1%
ValueCountFrequency (%)
10.406666671
< 0.1%
9.5333333331
< 0.1%
8.9733333331
< 0.1%
8.4633333331
< 0.1%
8.3633333331
< 0.1%
8.2933333331
< 0.1%
8.2833333331
< 0.1%
8.1666666672
< 0.1%
8.151
< 0.1%
8.1466666671
< 0.1%

15 km Czas
Real number (ℝ)

High correlation 

Distinct3471
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5089.3192
Minimum2868
Maximum7697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.5 KiB
2025-11-09T21:36:52.197148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2868
5-th percentile3887
Q14545
median5037
Q35586
95-th percentile6442
Maximum7697
Range4829
Interquartile range (IQR)1041

Descriptive statistics

Standard deviation764.68182
Coefficient of variation (CV)0.15025228
Kurtosis-0.25459819
Mean5089.3192
Median Absolute Deviation (MAD)518
Skewness0.23881433
Sum92732485
Variance584738.28
MonotonicityNot monotonic
2025-11-09T21:36:52.468171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
503735
 
0.2%
497819
 
0.1%
498919
 
0.1%
506019
 
0.1%
468518
 
0.1%
507318
 
0.1%
501117
 
0.1%
463817
 
0.1%
486017
 
0.1%
492417
 
0.1%
Other values (3461)18025
98.9%
ValueCountFrequency (%)
28681
 
< 0.1%
28891
 
< 0.1%
28981
 
< 0.1%
29291
 
< 0.1%
29712
< 0.1%
29741
 
< 0.1%
29873
< 0.1%
29901
 
< 0.1%
29942
< 0.1%
29971
 
< 0.1%
ValueCountFrequency (%)
76971
< 0.1%
74161
< 0.1%
74121
< 0.1%
74031
< 0.1%
73871
< 0.1%
73861
< 0.1%
73851
< 0.1%
73831
< 0.1%
73641
< 0.1%
73421
< 0.1%

15 km Tempo
Real number (ℝ)

High correlation 

Distinct1501
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8646536
Minimum-0.043333333
Maximum9.8866667
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size142.5 KiB
2025-11-09T21:36:52.716242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.043333333
5-th percentile4.42
Q15.1866667
median5.7566667
Q36.4666667
95-th percentile7.6266667
Maximum9.8866667
Range9.93
Interquartile range (IQR)1.28

Descriptive statistics

Standard deviation0.9620505
Coefficient of variation (CV)0.16404217
Kurtosis0.059939463
Mean5.8646536
Median Absolute Deviation (MAD)0.63
Skewness0.41057662
Sum106859.85
Variance0.92554117
MonotonicityNot monotonic
2025-11-09T21:36:52.952771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.76666666744
 
0.2%
5.6840
 
0.2%
5.7140
 
0.2%
5.72666666740
 
0.2%
5.51666666739
 
0.2%
5.48666666739
 
0.2%
5.72333333339
 
0.2%
5.6638
 
0.2%
5.7738
 
0.2%
5.54333333338
 
0.2%
Other values (1491)17826
97.8%
ValueCountFrequency (%)
-0.043333333331
< 0.1%
1.461
< 0.1%
21
< 0.1%
3.071
< 0.1%
3.2266666671
< 0.1%
3.2933333331
< 0.1%
3.3033333331
< 0.1%
3.331
< 0.1%
3.3366666671
< 0.1%
3.3633333331
< 0.1%
ValueCountFrequency (%)
9.8866666671
< 0.1%
9.881
< 0.1%
9.8666666671
< 0.1%
9.8266666671
< 0.1%
9.4066666671
< 0.1%
9.3566666671
< 0.1%
9.1633333331
< 0.1%
9.071
< 0.1%
9.061
< 0.1%
9.0133333331
< 0.1%

20 km Czas
Real number (ℝ)

High correlation 

Distinct4635
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6979.9875
Minimum3940
Maximum10115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.5 KiB
2025-11-09T21:36:53.183711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3940
5-th percentile5302
Q16209
median6869
Q37684
95-th percentile8948
Maximum10115
Range6175
Interquartile range (IQR)1475

Descriptive statistics

Standard deviation1088.3749
Coefficient of variation (CV)0.15592791
Kurtosis-0.23407328
Mean6979.9875
Median Absolute Deviation (MAD)724
Skewness0.30727535
Sum1.2718235 × 108
Variance1184559.9
MonotonicityNot monotonic
2025-11-09T21:36:53.404360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
679619
 
0.1%
686919
 
0.1%
684917
 
0.1%
680416
 
0.1%
665115
 
0.1%
689415
 
0.1%
677515
 
0.1%
689015
 
0.1%
659914
 
0.1%
668814
 
0.1%
Other values (4625)18062
99.1%
ValueCountFrequency (%)
39401
< 0.1%
39651
< 0.1%
39921
< 0.1%
40141
< 0.1%
40191
< 0.1%
40331
< 0.1%
40471
< 0.1%
40481
< 0.1%
40542
< 0.1%
40581
< 0.1%
ValueCountFrequency (%)
101151
< 0.1%
100541
< 0.1%
100271
< 0.1%
100251
< 0.1%
100171
< 0.1%
100141
< 0.1%
100111
< 0.1%
100081
< 0.1%
99922
< 0.1%
99911
< 0.1%

20 km Tempo
Real number (ℝ)

High correlation 

Distinct1780
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3022278
Minimum0.90333333
Maximum15.943333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.5 KiB
2025-11-09T21:36:53.634787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.90333333
5-th percentile4.6466667
Q15.4833333
median6.1233333
Q36.9766667
95-th percentile8.4933333
Maximum15.943333
Range15.04
Interquartile range (IQR)1.4933333

Descriptive statistics

Standard deviation1.1693432
Coefficient of variation (CV)0.18554441
Kurtosis0.82872254
Mean6.3022278
Median Absolute Deviation (MAD)0.73
Skewness0.70762784
Sum114832.89
Variance1.3673634
MonotonicityNot monotonic
2025-11-09T21:36:53.859875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5536
 
0.2%
5.83333333336
 
0.2%
5.79666666735
 
0.2%
5.73333333335
 
0.2%
5.9334
 
0.2%
5.80666666734
 
0.2%
5.6833
 
0.2%
5.9833
 
0.2%
5.77666666733
 
0.2%
6.3933
 
0.2%
Other values (1770)17879
98.1%
ValueCountFrequency (%)
0.90333333331
 
< 0.1%
2.5366666671
 
< 0.1%
3.3633333331
 
< 0.1%
3.3933333331
 
< 0.1%
3.441
 
< 0.1%
3.4866666671
 
< 0.1%
3.5333333332
< 0.1%
3.5733333331
 
< 0.1%
3.5833333333
< 0.1%
3.5866666672
< 0.1%
ValueCountFrequency (%)
15.943333331
< 0.1%
14.941
< 0.1%
13.473333331
< 0.1%
12.251
< 0.1%
11.883333331
< 0.1%
11.696666671
< 0.1%
11.673333331
< 0.1%
11.266666671
< 0.1%
11.156666671
< 0.1%
11.103333331
< 0.1%

Tempo Stabilność
Real number (ℝ)

High correlation 

Distinct7210
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.051033833
Minimum-0.34533333
Maximum0.62953333
Zeros1
Zeros (%)< 0.1%
Negative1232
Negative (%)6.8%
Memory size142.5 KiB
2025-11-09T21:36:54.108136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.34533333
5-th percentile-0.0039333333
Q10.0204
median0.041466667
Q30.071733333
95-th percentile0.1396
Maximum0.62953333
Range0.97486667
Interquartile range (IQR)0.051333333

Descriptive statistics

Standard deviation0.046224907
Coefficient of variation (CV)0.90576986
Kurtosis5.7647011
Mean0.051033833
Median Absolute Deviation (MAD)0.024266667
Skewness1.4781232
Sum929.88747
Variance0.0021367421
MonotonicityNot monotonic
2025-11-09T21:36:54.311838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0414666666774
 
0.4%
0.026216
 
0.1%
0.026615
 
0.1%
0.0368666666715
 
0.1%
0.0171333333315
 
0.1%
0.0144666666715
 
0.1%
0.0256666666715
 
0.1%
0.0313333333314
 
0.1%
0.0285333333314
 
0.1%
0.036413
 
0.1%
Other values (7200)18015
98.9%
ValueCountFrequency (%)
-0.34533333331
< 0.1%
-0.12781
< 0.1%
-0.11761
< 0.1%
-0.10233333331
< 0.1%
-0.10213333331
< 0.1%
-0.1021
< 0.1%
-0.087666666671
< 0.1%
-0.083066666671
< 0.1%
-0.075866666671
< 0.1%
-0.07541
< 0.1%
ValueCountFrequency (%)
0.62953333331
< 0.1%
0.52893333331
< 0.1%
0.43613333331
< 0.1%
0.42593333331
< 0.1%
0.42193333331
< 0.1%
0.39661
< 0.1%
0.39513333331
< 0.1%
0.37393333331
< 0.1%
0.36381
< 0.1%
0.3221
< 0.1%

Czas
Real number (ℝ)

High correlation 

Distinct4838
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7365.4034
Minimum4184
Maximum10551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.5 KiB
2025-11-09T21:36:54.528299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4184
5-th percentile5589
Q16547
median7237
Q38109
95-th percentile9457
Maximum10551
Range6367
Interquartile range (IQR)1562

Descriptive statistics

Standard deviation1155.3646
Coefficient of variation (CV)0.15686373
Kurtosis-0.2347939
Mean7365.4034
Median Absolute Deviation (MAD)769
Skewness0.31426034
Sum1.3420502 × 108
Variance1334867.4
MonotonicityNot monotonic
2025-11-09T21:36:54.737839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
782316
 
0.1%
718316
 
0.1%
728514
 
0.1%
752114
 
0.1%
692514
 
0.1%
710414
 
0.1%
715013
 
0.1%
720213
 
0.1%
719213
 
0.1%
716713
 
0.1%
Other values (4828)18081
99.2%
ValueCountFrequency (%)
41841
< 0.1%
42051
< 0.1%
42161
< 0.1%
42181
< 0.1%
42271
< 0.1%
42341
< 0.1%
42481
< 0.1%
42771
< 0.1%
42781
< 0.1%
42842
< 0.1%
ValueCountFrequency (%)
105512
< 0.1%
105492
< 0.1%
105471
< 0.1%
105431
< 0.1%
105411
< 0.1%
105401
< 0.1%
105392
< 0.1%
105372
< 0.1%
105351
< 0.1%
105331
< 0.1%

Tempo
Real number (ℝ)

High correlation 

Distinct4838
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8192332
Minimum3.3056807
Maximum8.3360986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size142.5 KiB
2025-11-09T21:36:54.978476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.3056807
5-th percentile4.4157383
Q15.1726317
median5.7177846
Q36.4067315
95-th percentile7.4717548
Maximum8.3360986
Range5.030418
Interquartile range (IQR)1.2340997

Descriptive statistics

Standard deviation0.9128266
Coefficient of variation (CV)0.15686373
Kurtosis-0.2347939
Mean5.8192332
Median Absolute Deviation (MAD)0.60756893
Skewness0.31426034
Sum106032.25
Variance0.83325241
MonotonicityNot monotonic
2025-11-09T21:36:55.192660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.18076953516
 
0.1%
5.67512048716
 
0.1%
5.75570830414
 
0.1%
5.9421663914
 
0.1%
5.47128071414
 
0.1%
5.61270443214
 
0.1%
5.64904795813
 
0.1%
5.69013194313
 
0.1%
5.68223117613
 
0.1%
5.6624792613
 
0.1%
Other values (4828)18081
99.2%
ValueCountFrequency (%)
3.3056806511
< 0.1%
3.322272261
< 0.1%
3.3309631031
< 0.1%
3.3325432571
< 0.1%
3.3396539461
< 0.1%
3.3451844831
< 0.1%
3.3562455561
< 0.1%
3.3791577781
< 0.1%
3.3799478551
< 0.1%
3.3846883152
< 0.1%
ValueCountFrequency (%)
8.3360986022
< 0.1%
8.3345184482
< 0.1%
8.3329382951
< 0.1%
8.3297779881
< 0.1%
8.3281978351
< 0.1%
8.3274077591
< 0.1%
8.3266176822
< 0.1%
8.3250375292
< 0.1%
8.3234573751
< 0.1%
8.3218772221
< 0.1%

Interactions

2025-11-09T21:36:42.680137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:14.108959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:17.116898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:21.520907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:23.823711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:25.928342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:28.133741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:30.619051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:32.727204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:35.300965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:38.926641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:43.085780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:14.571448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:17.531159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:21.745907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:24.030784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:26.221045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:28.348275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:30.811454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:32.930617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:35.613344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:39.287711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:43.393067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:14.865954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:17.898612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:21.942546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:24.233248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:26.451524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:28.542436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:31.003986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:33.115416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:35.915101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:39.625999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:43.754284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:15.175148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:18.386982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:22.229895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:24.450962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:26.647734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:28.753701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:31.213376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:33.308563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:36.328985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:39.933626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:44.042591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:15.397369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:19.164672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:22.431306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:24.629004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:26.828852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:29.088403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:31.403584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:33.496707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:36.712911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:40.188208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:44.309474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:15.684327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:19.458130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:22.625442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:24.793325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:27.008223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:29.274084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:31.584085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:33.678597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:36.984299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:40.831734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:44.595060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:15.880959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:20.006868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:22.813890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:24.960650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:27.191962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:29.461083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:31.758454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:33.862643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:37.313202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:41.176423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:44.926145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:16.193426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:20.291233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:23.031254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:25.157252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:27.386347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:29.907279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:31.944628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:34.047909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:37.676693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:41.463962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:45.276146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:16.417460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:20.588673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:23.244201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:25.356057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:27.584513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:30.102465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:32.157564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:34.250540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:38.108972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:41.848008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:45.630464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:16.711510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:20.897041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:23.430829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:25.550531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:27.768278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:30.271711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:32.344265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:34.602981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:38.395033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:42.224600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:45.924909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:16.930076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:21.218919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:23.625029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:25.759690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:27.959923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:30.451330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:32.538386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:34.904485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:38.604729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2025-11-09T21:36:42.456464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2025-11-09T21:36:55.391516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
10 km Czas10 km Tempo15 km Czas15 km Tempo20 km Czas20 km Tempo5 km Czas5 km TempoCzasKategoria wiekowaPłećTempoTempo Stabilność
10 km Czas1.0000.9910.9930.9520.9730.8730.9880.9880.9700.1320.3410.9700.288
10 km Tempo0.9911.0000.9920.9660.9800.8970.9590.9590.9770.1280.3320.9770.358
15 km Czas0.9930.9921.0000.9820.9900.9090.9710.9710.9880.1250.3410.9880.368
15 km Tempo0.9520.9660.9821.0000.9880.9420.9170.9170.9890.1150.3160.9890.491
20 km Czas0.9730.9800.9900.9881.0000.9570.9430.9430.9990.1200.3260.9990.473
20 km Tempo0.8730.8970.9090.9420.9571.0000.8280.8280.9560.1040.2580.9560.678
5 km Czas0.9880.9590.9710.9170.9430.8281.0001.0000.9410.1270.3100.9410.207
5 km Tempo0.9880.9590.9710.9170.9430.8281.0001.0000.9410.1270.3100.9410.207
Czas0.9700.9770.9880.9890.9990.9560.9410.9411.0000.1190.3231.0000.476
Kategoria wiekowa0.1320.1280.1250.1150.1200.1040.1270.1270.1191.0001.0000.1190.038
Płeć0.3410.3320.3410.3160.3260.2580.3100.3100.3231.0001.0000.3230.078
Tempo0.9700.9770.9880.9890.9990.9560.9410.9411.0000.1190.3231.0000.476
Tempo Stabilność0.2880.3580.3680.4910.4730.6780.2070.2070.4760.0380.0780.4761.000

Missing values

2025-11-09T21:36:46.886281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-09T21:36:47.564479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PłećKategoria wiekowa5 km Czas5 km Tempo10 km Czas10 km Tempo15 km Czas15 km Tempo20 km Czas20 km TempoTempo StabilnośćCzasTempo
0MM30971.03.2366671930.03.1966672929.03.3300004014.03.6166670.0254674216.03.330963
1MM20972.03.2400001955.03.2766672971.03.3866674047.03.5866670.0230004227.03.339654
2MM40969.03.2300001950.03.2700002971.03.4033334048.03.5900000.0242674234.03.345184
3MM20937.03.1233331885.03.1600002898.03.3766674058.03.8666670.0489334278.03.379948
4MM30990.03.3000001975.03.2833332997.03.4066674098.03.6700000.0246674302.03.398910
5MM201030.03.4333332063.03.4433333131.03.5600004263.03.7733330.0227334456.03.520581
6MM301013.03.3766672035.03.4066673099.03.5466674250.03.8366670.0304004462.03.525322
7MM201029.03.4300002063.03.4466673131.03.5600004282.03.8366670.0266674483.03.541914
8MM301035.03.4500002063.03.4266673133.03.5666674284.03.8366670.0260004490.03.547444
9MM201035.03.4500002063.03.4266673133.03.5666674290.03.8566670.0272004496.03.552185
PłećKategoria wiekowa5 km Czas5 km Tempo10 km Czas10 km Tempo15 km Czas15 km Tempo20 km Czas20 km TempoTempo StabilnośćCzasTempo
18211MM402398.07.9933334778.07.9333337272.08.3133339933.08.8700000.06020010530.08.319507
18212KK402419.08.0633334846.08.0900007383.08.4566679948.08.5500000.03653310535.08.323457
18213MM402145.07.1500004418.07.5766677139.09.0700009913.09.2466670.15566710537.08.325038
18214MM402416.08.0533334848.08.1066677386.08.4600009953.08.5566670.03726710537.08.325038
18215KK402422.08.0733334848.08.0866677385.08.4566679952.08.5566670.03640010539.08.326618
18216KK402423.08.0766674851.08.0933337387.08.4533339953.08.5533330.03580010540.08.327408
18217MM402147.07.1566674497.07.8333337111.08.7133339896.09.2833330.14520010547.08.332938
18218KK302266.07.5533334754.08.2933337364.08.70000010008.08.8133330.08373310549.08.334518
18219KK402153.07.1766674499.07.8200007113.08.7133339899.09.2866670.14446710549.08.334518
18220KK402308.07.6933334749.08.1366677254.08.3500009941.08.9566670.08006710551.08.336099